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‎DESCRIPTION

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Package: ipw
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Version: 1.0-11
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Date: 2015-08-18
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Version: 1.2
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Date: 2023-01-02
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Title: Estimate Inverse Probability Weights
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Authors@R: c(person(c("Ronald", "B."), "Geskus", role=c("aut", "cre"),
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, email="r.b.geskus@amc.uva.nl"),
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person(c("Willem","M."), "van der Wal", role=c("aut"),
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comment="maintainer until 2010"))
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Author: Ronald B. Geskus [aut, cre],
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Willem M. van der Wal [aut] (maintainer until 2010)
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Maintainer: Ronald B. Geskus <r.b.geskus@amc.uva.nl>
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Authors@R: c(person(c("Willem", "M."), "van der Wal", role = c("aut", "cre"), email = "willem@vanderwalresearch.com"),
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person(c("Ronald", "B."), "Geskus", role=c("aut"), email = "rgeskus@oucru.org", comment = "maintainer 2011-2022"))
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Author: Willem M. van der Wal [aut, cre],
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Ronald B. Geskus [aut] (maintainer 2011-2022)
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Maintainer: Willem M. van der Wal <willem@vanderwalresearch.com>
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Depends: R (>= 3.0.0)
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Imports: MASS, nnet, survival, geepack, graphics, methods, stats
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Suggests: nlme, survey, boot
@@ -19,8 +17,7 @@ Description: Functions to estimate the probability to receive the observed treat
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correct for informative censoring.
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Encoding: latin1
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License: GPL (>= 2)
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LazyData: true
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NeedsCompilation: no
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Packaged: 2015-08-18 13:57:44 UTC; rbgeskus
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Packaged: 2023-01-03 07:48:07 UTC; wmvdw
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Repository: CRAN
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Date/Publication: 2015-08-18 16:58:46
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Date/Publication: 2023-01-07 01:10:05 UTC

‎MD5

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4f8f69b99d106cbb70d8562f11729c16 *DESCRIPTION
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b73e62519f086868a5e0a4266bed8165 *DESCRIPTION
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2e22845f0b612dc7255c082909b3e485 *NAMESPACE
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f4ca3ddda31e0c2c3a296e76b40a2a92 *R/ipwplot.R
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a750e654e8061a1a0b515d3d05a96111 *R/ipwpoint.R
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df950ace2eb4c66af4cc834f0a2beb96 *R/ipwtm.R
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64a5b4534dce4f84588b819251957ee1 *R/ipwtm.R
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cdc21f06c11d712cda2b6077c5ab6ae8 *R/tstartfun.R
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afbe4209b3bb961ba3e41cbfe79ea507 *build/partial.rdb
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b53b8bbc825489376bb5b13f8ebd936c *data/basdat.rda
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a62c2f7cfa3a4f39a87ba53af0b20328 *data/haartdat.rda
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8ad4e902a3357de9ccc85901ce07d8c3 *data/healthdat.rda
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dd95e2bc0dc75d42abfe8136dbda29fa *data/timedat.rda
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f978f366db74f1b517f427146d66c5d4 *inst/CITATION
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4e441772cc14aff5f0fc711b903a49a3 *man/basdat.Rd
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11ef2ab9d55369eb08e5762747c40df2 *man/haartdat.Rd
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1697ed76b00a2b8fa9ec17c69c7a05bf *man/healthdat.Rd
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1a27742d4f6d867b4c85c67f941bf2a9 *man/ipwplot.Rd
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27ce412e89030c0af2cf60e25395b047 *man/ipwpoint.Rd
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1a34142725ebe9034d51deb2006a0d9c *man/ipwtm.Rd
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d98aafc8fce48f43a1cd2d6dd7f2fe52 *man/timedat.Rd
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fef4fbfb35b11ed1dbb3549c515bd74b *man/tstartfun.Rd
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205d49c52455860e14af67ac6705ce60 *inst/CITATION
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548818cb0fda0b5f56cf6bee87cdd3ba *man/basdat.Rd
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8bfdce30774155d8a4bafd1d22bef6ed *man/haartdat.Rd
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18bdde99298a10d37e762d8c3734a986 *man/healthdat.Rd
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8f1793dc2bcc463439e8d538d7e4daa3 *man/ipwplot.Rd
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df5a62081b0ad79d4f30ee6e4afdbb71 *man/ipwpoint.Rd
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304f7fc8cddd6c8d4db705e1c2695c97 *man/ipwtm.Rd
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db3a0eb785e56e5187bb9a1853c22411 *man/timedat.Rd
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040eb5c7623824777182663c50184f6f *man/tstartfun.Rd

‎R/ipwtm.R

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Original file line numberDiff line numberDiff line change
@@ -19,16 +19,17 @@ ipwtm <- function(
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if (!("exposure" %in% names(tempcall))) stop("No exposure variable specified")
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if (!("family" %in% names(tempcall)) | ("family" %in% names(tempcall) & !(tempcall$family %in% c("binomial", "survival", "multinomial", "ordinal", "gaussian")))) stop("No valid family specified (\"binomial\", \"survival\", \"multinomial\", \"ordinal\", \"gaussian\")")
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if (tempcall$family == "binomial") {if(!(tempcall$link %in% c("logit", "probit", "cauchit", "log", "cloglog"))) stop("No valid link function specified for family = binomial (\"logit\", \"probit\", \"cauchit\", \"log\", \"cloglog\")")}
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if (tempcall$family == "ordinal" ) {if(!(tempcall$link %in% c("logit", "probit", "cauchit", "cloglog"))) stop("No valid link function specified for family = binomial (\"logit\", \"probit\", \"cauchit\", \"cloglog\")")}
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if (tempcall$family == "ordinal" ) {if(!(tempcall$link %in% c("logit", "probit", "cauchit", "cloglog"))) stop("No valid link function specified for family = ordinal (\"logit\", \"probit\", \"cauchit\", \"cloglog\")")}
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if (!("denominator" %in% names(tempcall))) stop("No denominator model specified")
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if (!is.null(tempcall$numerator) & !is(eval(tempcall$numerator), "formula")) stop("Invalid numerator formula specified")
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if (!is.null(tempcall$denominator) & !is(eval(tempcall$denominator), "formula")) stop("Invalid denominator formula specified")
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if (!("id" %in% names(tempcall))) stop("No patient id specified")
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if (tempcall$family == "survival" & !("tstart" %in% names(tempcall))) stop("No tstart specified, is necessary for family = \"survival\"")
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if (!("timevar" %in% names(tempcall))) stop("No timevar specified")
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if (!("type" %in% names(tempcall))) stop("No type specified (\"first\" or \"all\")")
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if (!(tempcall$type %in% c("first", "all"))) stop("No type specified (\"first\" or \"all\")")
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if (!(tempcall$type %in% c("first", "all", "cens"))) stop("No type specified (\"first\", \"all\" or \"cens\")")
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if (tempcall$family %in% c("survival", "multinomial", "ordinal") & tempcall$type == "all") stop(paste("Type \"all\" not yet implemented for family = ", deparse(tempcall$family, width.cutoff = 500), sep = ""))
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if (tempcall$family %in% c("multinomial", "ordinal", "gaussian") & tempcall$type == "cens") stop(paste("Type \"cens\" not yet implemented for family = ", deparse(tempcall$family, width.cutoff = 500), sep = ""))
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if (tempcall$family %in% c("gaussian") & tempcall$type == "first") stop(paste("Type \"first\" not implemented for family = ", deparse(tempcall$family, width.cutoff = 500), sep = ""))
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if (tempcall$family %in% c("gaussian") & !("numerator" %in% names(tempcall))) stop("Numerator necessary for family = \"gaussian\"")
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if (!("data" %in% names(tempcall))) stop("No data specified")
@@ -51,17 +52,17 @@ ipwtm <- function(
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exposure = data[,as.character(tempcall$exposure)]
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)
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#make selection variable, time points up to first switch from lowest value, or all time points
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if (type == "first" & (family == "binomial" | family == "survival"))
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if (type %in% c("first", "cens") & (family == "binomial" | family == "survival"))
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{tempdat$selvar <- do.call("c", lapply(split(tempdat$exposure, tempdat$id),function(x)if (!is.na(match(1, x))) return(c(rep(1,match(1, x)),rep(0,length(x)-match(1, x)))) else return(rep(1,length(x)))))}
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if (type == "first" & (family == "multinomial" | family == "ordinal")){
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if (type %in% c("first", "cens") & (family == "multinomial" | family == "ordinal")){
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z <- unique(tempdat$exposure)[unique(tempdat$exposure) != sort(unique(tempdat$exposure))[1]]
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min2 <- function(x)ifelse(min(is.na(unique(x))) == 1, NA, min(x, na.rm = TRUE))
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tempdat$selvar <- do.call("c", lapply(split(tempdat$exposure, tempdat$id),function(x)if (!is.na(min2(match(z, x)))) return(c(rep(1,min2(match(z, x))),rep(0,length(x)-min2(match(z, x))))) else return(rep(1,length(x)))))
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}
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if (type == "all")
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{tempdat$selvar <- rep(1, nrow(tempdat))}
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#weights binomial, type "first"
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if (tempcall$family == "binomial" & tempcall$type == "first") {
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if (tempcall$family == "binomial" & tempcall$type %in% c("first", "cens")) {
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if(tempcall$link == "logit") lf <- binomial(link = logit)
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if(tempcall$link == "probit") lf <- binomial(link = probit)
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if(tempcall$link == "cauchit") lf <- binomial(link = cauchit)
@@ -78,7 +79,8 @@ ipwtm <- function(
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...)
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tempdat$p.numerator <- vector("numeric", nrow(tempdat))
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tempdat$p.numerator[tempdat$exposure == 0 & tempdat$selvar == 1] <- 1 - predict.glm(mod1, type = "response")[tempdat$exposure[tempdat$selvar == 1] == 0]
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tempdat$p.numerator[tempdat$exposure == 1 & tempdat$selvar == 1] <- predict.glm(mod1, type = "response")[tempdat$exposure[tempdat$selvar == 1] == 1]
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if(type == "first"){tempdat$p.numerator[tempdat$exposure == 1 & tempdat$selvar == 1] <- predict.glm(mod1, type = "response")[tempdat$exposure[tempdat$selvar == 1] == 1]}
83+
if(type == "cens"){tempdat$p.numerator[tempdat$exposure == 1 & tempdat$selvar == 1] <- 1 - predict.glm(mod1, type = "response")[tempdat$exposure[tempdat$selvar == 1] == 1]}
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tempdat$p.numerator[tempdat$selvar == 0] <- 1
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tempdat$w.numerator <- unlist(lapply(split(tempdat$p.numerator, tempdat$id), function(x)cumprod(x)))
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mod1$call$formula <- eval(parse(text = paste(deparse(tempcall$exposure, width.cutoff = 500), deparse(tempcall$numerator, width.cutoff = 500), sep = "")))
@@ -95,7 +97,8 @@ ipwtm <- function(
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...)
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tempdat$p.denominator <- vector("numeric", nrow(tempdat))
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tempdat$p.denominator[tempdat$exposure == 0 & tempdat$selvar == 1] <- 1 - predict.glm(mod2, type = "response")[tempdat$exposure[tempdat$selvar == 1] == 0]
98-
tempdat$p.denominator[tempdat$exposure == 1 & tempdat$selvar == 1] <- predict.glm(mod2, type = "response")[tempdat$exposure[tempdat$selvar == 1] == 1]
100+
if(type == "first"){tempdat$p.denominator[tempdat$exposure == 1 & tempdat$selvar == 1] <- predict.glm(mod2, type = "response")[tempdat$exposure[tempdat$selvar == 1] == 1]}
101+
if(type == "cens"){tempdat$p.denominator[tempdat$exposure == 1 & tempdat$selvar == 1] <- 1 - predict.glm(mod2, type = "response")[tempdat$exposure[tempdat$selvar == 1] == 1]}
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tempdat$p.denominator[tempdat$selvar == 0] <- 1
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tempdat$w.denominator <- unlist(lapply(split(tempdat$p.denominator, tempdat$id), function(x)cumprod(x)))
101104
mod2$call$formula <- eval(parse(text = paste(deparse(tempcall$exposure, width.cutoff = 500), deparse(tempcall$denominator, width.cutoff = 500), sep = "")))
@@ -163,12 +166,13 @@ ipwtm <- function(
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temp$bashaz.cum.numerator <- NULL
164167
tempdat <- merge(tempdat, temp, by = "timevar", all.x = TRUE);rm(temp)
165168
tempdat <- tempdat[order(tempdat$id, tempdat$timevar),]
166-
tempdat$risk.numerator[tempdat$selvar == 1] <- predict(mod1, type="risk", centered = TRUE)
169+
tempdat$risk.numerator[tempdat$selvar == 1] <-predict(mod1, type="risk", centered = TRUE)
167170
tempdat$hazard.numerator[tempdat$selvar == 1] <- with(tempdat[tempdat$selvar == 1,], bashaz.numerator*risk.numerator)
168171
tempdat$p.numerator[with(tempdat, selvar == 1 & exposure == 0)] <- with(tempdat[with(tempdat, selvar == 1 & exposure == 0),], exp(-1*bashaz.numerator*risk.numerator))
169-
tempdat$p.numerator[with(tempdat, selvar == 1 & exposure == 1)] <- 1 - with(tempdat[with(tempdat, selvar == 1 & exposure == 1),], exp(-1*bashaz.numerator*risk.numerator))
172+
if(type == "first"){tempdat$p.numerator[with(tempdat, selvar == 1 & exposure == 1)] <- 1 - with(tempdat[with(tempdat, selvar == 1 & exposure == 1),], exp(-1*bashaz.numerator*risk.numerator))}
173+
if(type == "cens"){tempdat$p.numerator[with(tempdat, selvar == 1 & exposure == 1)] <- with(tempdat[with(tempdat, selvar == 1 & exposure == 1),], exp(-1*bashaz.numerator*risk.numerator))}
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tempdat$p.numerator[tempdat$selvar == 0] <- 1
171-
tempdat$w.numerator <- unsplit(lapply(split(tempdat$p.numerator, tempdat$id), function(x) cumprod(x)), tempdat$id)
175+
tempdat$w.numerator <- unsplit(lapply(split(tempdat$p.numerator, tempdat$id), function(x)cumprod(x)), tempdat$id)
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mod1$call$formula <- eval(parse(text = paste("Surv(", deparse(tempcall$tstart), ", ", deparse(tempcall$timevar, width.cutoff = 500), ", ", deparse(tempcall$exposure, width.cutoff = 500), ") ", deparse(tempcall$numerator, width.cutoff = 500), sep = "")))
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mod1$call$data <- tempcall$data
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}
@@ -189,10 +193,11 @@ ipwtm <- function(
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temp$bashaz.cum.denominator <- NULL
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tempdat <- merge(tempdat, temp, by = "timevar", all.x = TRUE);rm(temp)
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tempdat <- tempdat[order(tempdat$id, tempdat$timevar),]
192-
tempdat$risk.denominator[tempdat$selvar == 1] <- predict(mod2, type="risk", centered = TRUE)
196+
tempdat$risk.denominator[tempdat$selvar == 1] <-predict(mod2, type="risk", centered = TRUE)
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tempdat$hazard.denominator[tempdat$selvar == 1] <- with(tempdat[tempdat$selvar == 1,], bashaz.denominator*risk.denominator)
194198
tempdat$p.denominator[with(tempdat, selvar == 1 & exposure == 0)] <- with(tempdat[with(tempdat, selvar == 1 & exposure == 0),], exp(-1*bashaz.denominator*risk.denominator))
195-
tempdat$p.denominator[with(tempdat, selvar == 1 & exposure == 1)] <- 1 - with(tempdat[with(tempdat, selvar == 1 & exposure == 1),], exp(-1*bashaz.denominator*risk.denominator))
199+
if(type == "first"){tempdat$p.denominator[with(tempdat, selvar == 1 & exposure == 1)] <- 1 - with(tempdat[with(tempdat, selvar == 1 & exposure == 1),], exp(-1*bashaz.denominator*risk.denominator))}
200+
if(type == "cens"){tempdat$p.denominator[with(tempdat, selvar == 1 & exposure == 1)] <- with(tempdat[with(tempdat, selvar == 1 & exposure == 1),], exp(-1*bashaz.denominator*risk.denominator))}
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tempdat$p.denominator[tempdat$selvar == 0] <- 1
197202
tempdat$w.denominator <- unsplit(lapply(split(tempdat$p.denominator, tempdat$id), function(x)cumprod(x)), tempdat$id)
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mod2$call$formula <- eval(parse(text = paste("Surv(", deparse(tempcall$tstart), ", ", deparse(tempcall$timevar, width.cutoff = 500), ", ", deparse(tempcall$exposure, width.cutoff = 500), ") ", deparse(tempcall$denominator, width.cutoff = 500), sep = "")))

‎build/partial.rdb

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‎inst/CITATION

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@@ -9,12 +9,12 @@ citEntry(entry = "Article",
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volume = "43",
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number = "13",
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pages = "1--23",
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url = "http://www.jstatsoft.org/v43/i13/",
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doi = "10.18637/jss.v043.i13",
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textVersion =
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paste("Willem M. van der Wal, Ronald B. Geskus (2011).",
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"ipw: An R Package for Inverse Probability Weighting.",
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"Journal of Statistical Software, 43(13), 1-23.",
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"URL http://www.jstatsoft.org/v43/i13/.")
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"DOI 10.18637/jss.v043.i13")
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)
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‎man/basdat.Rd

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@@ -22,12 +22,12 @@ These simulated data are used together with data in \code{\link{timedat}} in a d
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}
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\references{
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Van der Wal W.M. & Geskus R.B. (2011). ipw: An R Package for Inverse Probability Weighting. \emph{Journal of Statistical Software}, \bold{43}(13), 1-23. \url{http://www.jstatsoft.org/v43/i13/}.
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Van der Wal W.M. & Geskus R.B. (2011). ipw: An R Package for Inverse Probability Weighting. \emph{Journal of Statistical Software}, \bold{43}(13), 1-23. \doi{10.18637/jss.v043.i13}.
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Van der Wal W.M., Prins M., Lumbreras B. & Geskus R.B. (2009). A simple G-computation algorithm to quantify the causal effect of a secondary illness on the progression of a chronic disease. \emph{Statistics in Medicine}, \bold{28}(18), 2325-2337.
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}
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\author{Willem M. van der Wal \email{w.m.vanderwal@amc.uva.nl}}
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\author{Willem M. van der Wal \email{willem@vanderwalresearch.com}, Ronald B. Geskus \email{rgeskus@oucru.org}}
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\seealso{\code{\link{basdat}}, \code{\link{haartdat}}, \code{\link{ipwplot}}, \code{\link{ipwpoint}}, \code{\link{ipwtm}}, \code{\link{timedat}}, \code{\link{tstartfun}}.}
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‎man/haartdat.Rd

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@@ -28,10 +28,10 @@ Patients can initiate HAART at \code{fuptime=0}. Therefore, to allow the fitting
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}
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\references{
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Van der Wal W.M. & Geskus R.B. (2011). ipw: An R Package for Inverse Probability Weighting. \emph{Journal of Statistical Software}, \bold{43}(13), 1-23. \url{http://www.jstatsoft.org/v43/i13/}.
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Van der Wal W.M. & Geskus R.B. (2011). ipw: An R Package for Inverse Probability Weighting. \emph{Journal of Statistical Software}, \bold{43}(13), 1-23. \doi{10.18637/jss.v043.i13}.
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}
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\author{Willem M. van der Wal \email{w.m.vanderwal@amc.uva.nl}}
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\author{Willem M. van der Wal \email{willem@vanderwalresearch.com}, Ronald B. Geskus \email{rgeskus@oucru.org}}
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\seealso{\code{\link{basdat}}, \code{\link{haartdat}}, \code{\link{ipwplot}}, \code{\link{ipwpoint}}, \code{\link{ipwtm}}, \code{\link{timedat}}, \code{\link{tstartfun}}.}
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‎man/healthdat.Rd

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@@ -22,10 +22,10 @@ In these simulated data, IQ is a confounder for the effect of income on health.
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}
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\references{
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Van der Wal W.M. & Geskus R.B. (2011). ipw: An R Package for Inverse Probability Weighting. \emph{Journal of Statistical Software}, \bold{43}(13), 1-23. \url{http://www.jstatsoft.org/v43/i13/}.
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Van der Wal W.M. & Geskus R.B. (2011). ipw: An R Package for Inverse Probability Weighting. \emph{Journal of Statistical Software}, \bold{43}(13), 1-23. \doi{10.18637/jss.v043.i13}.
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}
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\author{Willem M. van der Wal \email{w.m.vanderwal@amc.uva.nl}}
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\author{Willem M. van der Wal \email{willem@vanderwalresearch.com}, Ronald B. Geskus \email{rgeskus@oucru.org}}
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\seealso{\code{\link{basdat}}, \code{\link{haartdat}}, \code{\link{healthdat}}, \code{\link{ipwplot}}, \code{\link{ipwpoint}}, \code{\link{ipwtm}}, \code{\link{timedat}}, \code{\link{tstartfun}}.}
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‎man/ipwplot.Rd

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}
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\references{
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Van der Wal W.M. & Geskus R.B. (2011). ipw: An R Package for Inverse Probability Weighting. \emph{Journal of Statistical Software}, \bold{43}(13), 1-23. \url{http://www.jstatsoft.org/v43/i13/}.
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Van der Wal W.M. & Geskus R.B. (2011). ipw: An R Package for Inverse Probability Weighting. \emph{Journal of Statistical Software}, \bold{43}(13), 1-23. \doi{10.18637/jss.v043.i13}.
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}
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\author{Willem M. van der Wal \email{w.m.vanderwal@amc.uva.nl}}
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\author{Willem M. van der Wal \email{willem@vanderwalresearch.com}, Ronald B. Geskus \email{rgeskus@oucru.org}}
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\seealso{\code{\link{basdat}}, \code{\link{haartdat}}, \code{\link{ipwplot}}, \code{\link{ipwpoint}}, \code{\link{ipwtm}}, \code{\link{timedat}}, \code{\link{tstartfun}}.}
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‎man/ipwpoint.Rd

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Robins, J.M., Hernán, M.A. & Brumback, B.A. (2000). Marginal structural models and causal inference in epidemiology. \emph{Epidemiology}, \bold{11}, 550-560.
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Van der Wal W.M. & Geskus R.B. (2011). ipw: An R Package for Inverse Probability Weighting. \emph{Journal of Statistical Software}, \bold{43}(13), 1-23. \url{http://www.jstatsoft.org/v43/i13/}.
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Van der Wal W.M. & Geskus R.B. (2011). ipw: An R Package for Inverse Probability Weighting. \emph{Journal of Statistical Software}, \bold{43}(13), 1-23. \doi{10.18637/jss.v043.i13}.
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}
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55-
\author{Willem M. van der Wal \email{w.m.vanderwal@amc.uva.nl}}
55+
\author{Willem M. van der Wal \email{willem@vanderwalresearch.com}, Ronald B. Geskus \email{rgeskus@oucru.org}}
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\seealso{\code{\link{basdat}}, \code{\link{haartdat}}, \code{\link{ipwplot}}, \code{\link{ipwpoint}}, \code{\link{ipwtm}}, \code{\link{timedat}}, \code{\link{tstartfun}}.}
5858

@@ -99,23 +99,24 @@ coef(msm)
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confint(msm)
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\dontrun{
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## Not run:
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#Compute basic bootstrap confidence interval .
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require(boot)
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boot.fun <- function(dat, index){
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coef(glm(
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formula = y ~ a,
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data = dat[index,],
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weights = ipwpoint(
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exposure = a,
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family = "gaussian",
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numerator = ~ 1,
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denominator = ~ l,
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data = dat[index,])$ipw.weights))[2]
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}
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bootres <- boot(simdat, boot.fun, 499);bootres
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boot.ci(bootres, type = "basic")
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}
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#require(boot)
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#boot.fun <- function(dat, index){
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# coef(glm(
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# formula = y ~ a,
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# data = dat[index,],
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# weights = ipwpoint(
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# exposure = a,
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# family = "gaussian",
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# numerator = ~ 1,
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# denominator = ~ l,
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# data = dat[index,])$ipw.weights))[2]
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# }
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#bootres <- boot(simdat, boot.fun, 499);bootres
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#boot.ci(bootres, type = "basic")
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## End(Not run)
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}
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